Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines in the Three Gorges Reservoir Area, China

The landslide development laws vary in different landslide-prone areas, hence the susceptibility models often perform in varied ways in different regions. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). These landslides seriously threaten the safety of local residents and their property. It is crucial to find the model that can generate a landslide susceptibility map with higher accuracy in the TGRA. The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA.

The Wushan segment of TGRA was selected as a case study, which is located in the middle reaches of the TGRA, the southwest of China. In this study, 165 landslides were identified and 14 landslide causal factors were constructed from different data sources at first, including altitude, slope, aspect, curvature, plan curvature, profile curvature, stream power index, topographic wetness index (TWI), terrain roughness index, lithology, bedding structure, distance to faults, distance to rivers, and distance to gully. Subsequently, multicollinearity analysis and information gain ratio model were applied to select landslide causal factors. After removing five factors (altitude, TWI, profile curvature, plan curvature, curvature), the landslide susceptibility mapping using the calculated results of four models, which were support vector machines (SVM), artificial neural networks, classification and regression tree, and logistic regression. Finally, the accuracy of the four models was evaluated and compared using the accuracy statistic methods and the receiver operating characteristic (ROC). The results of accuracy analysis showed that the SVM model performed the best. At the same time, the SVM performance behavior for susceptibility modelling in other areas were collected. In these regions, the accuracy of SVM was always larger than 0.8. We could see that SVM performed acceptably in different regions, and thus it can be used as a recommended model in TGRA and other landslide-prone regions.

In this study area, a total of 62% of the landslides were within 300 m from the Yangtze River, and the distance to rivers was the most important factor. The impoundment of the TGRA impacted the landslide development in three aspects: (1) the long-term immersion of reservoir water gradually reducing the strength of rock (soil) at the saturated zone (mostly near the Yangtze river), reducing the resistance force of landslide; (2) the strong dynamic action of water enhancing the lateral erosion on the bank slope, changing the slope shape, and thus reducing the slope stability; (3) the periodic fluctuation of the reservoir water making the self-weight, static, and dynamic water pressure of the landslide change, which could increase the resistance force or reduce the sliding force of the landslide and even cause overall instability and damage. Hence, in order to reduce the losses caused by landslides in TGRA, we should pay more attention to the early warning of reservoir bank landslides.